Sense & Sensibility for Wireless Networks. Hari Balakrishnan Department of EECS and CSAIL M.I.T. nms.csail.mit.edu/~hari

Transcription

1 Sense & Sensibility for Wireless Networks Hari Balakrishnan Department of EECS and CSAIL M.I.T. nms.csail.mit.edu/~hari

2 The Era of Truly Mobile Computing 2000 Internet Users (Millions) Desktop Users Source: Morgan Stanley Research

3 5B mobile phones in the world Exceeds #people with shoes, toilets, electricity! 1B with broadband capability Gigabytes/user/month Average Demand Per Cellular User 1 hr HD video 1 hr netflix Projection: Rysavy Research, 2010 Average wired US: 18 GB/mo (AT&T) UK: GB/month (2011) Survey by TeleNav

4 Mobile Apps All Around Us Public Safety Healthcare Transportation Apps demand robust, high-performance wireless networks Difficult as load and node density increase Entertainment & Social Lives Difficult as channel conditions change with Sensor-rich time systems Smart Homes

5 Truly Mobile Devices Often switch between static and mobile Exhibit a variety of mobility modes Move through different environments

6 Mobility à Time-varying Channels & Topology Measured SNR (db) Slow walk Shaded area detail Time (s) Time (ms) Bit error rate Time (s)

7 Example: a/g 8 bit rates: 6, 9, 12, 18, 24, 36, 48, 54 Mbps symbol sets (= constellation) Two possible codes per constellation (Rate 1/2 or 3/4 conv. codes) n: 32 possible [constell n X code X stream] combinations How to determine the best bit rate to transmit a packet?

8 Bit Rate Selection Measure wireless channel Frame loss rate at different bit rates [SampleRate, RRAA, ARF, ] Signal-to-noise ratio (SNR) [RBAR, CHARM,, 3GPP] Determine best bit rate given measurement Problem: Measurements quickly outdated over time-varying channels

9 The Big Idea: Sensors on Devices Proximity Sensor Camera Accelerometer GPS Compass Gyro Ambient Light Sensor Microphone Many, many, applications

10 Proximity Sensor Camera The Opportunity: Sensors Protocol Stack Applica<on Accelerometer Compass Gyro GPS Transport Network Link/MAC PHY Radio Largely ignored by protocols! Ambient Light Sensor Microphone

11 Integrate Sensor Hints into the Network Stack GPS Compass Accl Gyro Sensor Hints Protocol Stack Applica<on Transport Network MAC PHY Radio Position Movement Direction Speed Use hints to adapt to different mobility modes differently Mobility hint protocol Adapt to hints from neighbors

12 Integrate Sensor Hints into the Network Stack Protocol Stack GPS Compass Accl Gyro Speed Heading Walking Movement Applica<on Transport Network MAC PHY Vehicular Rou<ng AP Associa<on Rate Selec<on Radio L. Ravindranath, HB, C. Newport, S. Madden, Improving wireless network performance using sensor hints, NSDI 2011

13 Integrate Sensor Hints into the Network Stack GPS Compass Accl Gyro Speed Loca<on Heading Walking Movement Speed Protocol Stack Applica<on Transport Network MAC PHY Radio Network Monitoring Vehicular Rou<ng Topo maintenance AP Associa<on Rate Selec<on Packet Scheduling Cyclic Prefix Power Saving L. Ravindranath, HB, C. Newport, S. Madden, Improving wireless network performance using sensor hints, NSDI 2011

14 Extracting Hints: Movement Example Is the device is static or moving? 3- axis accel Movement Reliably detect movement within ms on commodity Android devices

15 Static v. Mobile Loss Performance Probability that packet i is lost given packet i- k is lost Losses more bursty when node is moving than when static. RapidSample: protocol to adapt while moving (small history)

16 Hint-Aware Rate Selection Accl Movement Wireless Protocol Stack Applica<on Transport Network Rate Selec<on PHY Wireless Radio RapidSample when moving Quick response to channel changes SampleRate when static Slower response to channel changes Use two different protocols, switching between them Movement using sensor hints

17 Hint- aware Experimental Results Rate selection: 40-60% faster than previous protocols

18 Sensor-Augmented Protocols: Results Access point selection 40% fewer handoff disruptions than current scheme Mobile topology maintenance 20x reduction in probe bandwidth for Vehicular network routing 2-5x longer connection time

19 Picking a Good Access Point (AP) Scan Scan Infrequent scans (because it wastes channel)

20 Walking-Aware Association How to maximize mobile throughput? 1. Static Stop Scanning 2. Walking Scan Periodically 3. Walking to Static Scan once

21 Heading-Aware Association How to minimize handoffs (disruptions)? Heading Use direc)on of movement to pick AP Ø Background Android applica<on Ø Training: WiFi scans + Heading hint Ø Query the model with current AP and heading hint

22 Hint-Aware AP Selection Android implementation 30 traces; Static + Moving Throughput # handoffs 30% higher median throughput 40% median reduction in handoffs

23 Potential Concerns Energy Accelerometer, compass are cheap but GPS is not Triggered sensing + adapt sample rates Calibration across device types Movement hint required no calibration Walking hint required tuning Why not network measurement (SNR/RSSI)? Variable and hard to measure reliably Privacy

24 GPS Compass Accl Gyro Speed Loca<on Heading Walking Movement Speed Sensor Hints External Information in Network Protocols Protocol Stack Applica<on Transport Network MAC PHY Radio Network Monitoring Vehicular Rou<ng Topo maintenance AP Associa<on Rate Selec<on Packet Scheduling Cyclic Prefix Power Saving L. Ravindranath, HB, C. Newport, S. Madden, Improving wireless network performance using sensor hints, NSDI 2011

25 Embed Sense into Wireless Protocols! Truly mobile devices will soon be dominant Variety of mobility modes poses problems Sensors on mobile devices give us a great opportunity to develop new protocols On-going: apply activity abstraction more widely Sensor-augmented protocol architecture Senseless to design mobile networks any other way! Acks: Lenin Ravindranath, Sam Madden, Cal Newport (collaborators), NSF, and Om Malik for the title idea (